Here, we describe the CNN that generates the personality vectors. Given a user's comment, which is a text \(S = [w_1, ..., w_n]\)  composed of \(n\)  words, each word \(w_i\)  is represented as a word embedding \(\vec{\mathbf {w}}_i \in \mathbb {R}^{d_{em}}\)  using the pre-trained FastText embeddings [1]}. A single-layered CNN is then modeled on this input sequence \(S\)  [2]}. First, a convolutional layer is applied having three filters \(F_{[1,2,3]} \in \mathbb {R}^{d_{em} \times h_{[1,2,3]}}\)  of heights \(h_{[1,2,3]}\) , respectively. For each \(k \in \lbrace 1,2,3 \rbrace \) , filter \(F_k\)  slides across \(S\)  and extracts \(h_k\) -gram features at each instance. This creates a feature map vector \(\vec{\mathbf {m}}_k\)  of size \(\mathbb {R}^{\mid S \mid - h_k + 1}\) , whose each entry \(m_{k,j}\)  is obtained as:
\(m_{k,j} = \alpha ( \ {F_k \cdot S_{[j:j+h_k-1]} + b_k} \ )\) 
